Robust Control Framework Based on Input-Output Models Enhanced with Uncertainty Estimation

Mariana RODRIGUEZ-JARA, Hilario FLORES-MEJIA, Alejandra VELASCO-PEREZ, Hector PUEBLA

Abstract

A versatile and simple robust model-based control framework for regulating a class of (bio)-chemical processes is introduced. The proposed control framework departs from a simple low-order model which is enhanced by estimating model uncertainties due to model reduction, uncertain model parameters, and external disturbances. Three robust model-based control schemes are then formulated based on the enhanced simple input-output model. The proposed robust framework provides the robustness of two well-known model-based control approaches using a simple low-order model. A benchmark model of microalgae production is employed for illustrating the controller performance. Moreover, a classical PI controller and a nonlinear MPC controller are also designed and applied for comparison purposes. Numerical results show that the proposed simple robust model-based controllers are able to regulate the controlled variable to the desired reference despite external disturbances and set-point changes. Furthermore, the proposed controllers feature an acceptable performance in comparison with two of the most widely accepted controllers in the control engineering community for controlling (bio)- chemical processes.

Keywords

Robust control, Model-based control, Input-output models, State observers, (Bio)-chemical processes.

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